# -----------------------------------
# Hierarchical Emulator Documentation
# -----------------------------------
#' @title Hierarchical Bayes Linear Emulator
#'
#' @description Creates a univariate emulator with hierarchical structure.
#'
#' This object does not differ extensively from the standard \code{\link{Emulator}} object, so
#' most of the functionality will not be listed here: the main difference is that
#' it allows for the variance structure of the emulator to be modified by a higher
#' order object. The typical usage is to create a variance emulator, whose predictions
#' inform the behaviour of a mean emulator with regard to a stochastic process.
#'
#' @name HierarchicalEmulator
#'
#' @section Constructor: \code{HierarchicalEmulator$new(basis_f, beta, u, ranges, ...)}
#'
#' @section Arguments:
#'
#' For details of shared arguments, see \code{\link{Emulator}}.
#'
#' \code{s_diag} The function that modifies the structure of the Bayes Linear adjustment.
#'
#' \code{samples} A numeric vector that indicates how many replicates each of the training
#' points has.
#'
#' \code{em_type} Whether the emulator is emulating a mean surface or a variance surface.
#'
#' @section Constructor Details:
#'
#' See \code{\link{Emulator}}: the constructor structure is the same save for the
#' new arguments discussed above.
#'
#' @section Accessor Methods:
#'
#' \code{get_exp(x, samps = NULL)} Similar in form to the normal Emulator method; the
#' \code{samps} argument allows the estimation of summary statistics derived from
#' multiple realisations.
#'
#' \code{get_cov(x, xp = NULL, full = FALSE, samps = NULL)} Differences here are in
#' line with those described in \code{get_exp}.
#'
#' @section Object Methods:
#'
#' Identical to those of \code{\link{Emulator}}: the one internal difference is that
#' \code{adjust} returns a HierarchicalEmulator rather than a standard one.
#'
#' @references Goldstein & Vernon (2016), in preparation
#' @export
#'
#' @examples
#' h_em <- emulator_from_data(BirthDeath$training, c('Y'),
#' list(lambda = c(0, 0.08), mu = c(0.04, 0.13)), emulator_type = "variance")
#' names(h_em) # c("expectation', 'variance')
NULL
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